Dataset Card for Set5

Dataset Summary

Set5 is a evaluation dataset with 5 RGB images for the image super resolution task. The 5 images of the dataset are (“baby”, “bird”, “butterfly”, “head”, “woman”).

Install with pip:

pip install datasets super-image

Evaluate a model with the super-image library:

from datasets import load_dataset
from super_image import EdsrModel
from super_image.data import EvalDataset, EvalMetrics

dataset = load_dataset('eugenesiow/Set5', 'bicubic_x2', split='validation')
eval_dataset = EvalDataset(dataset)
model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
EvalMetrics().evaluate(model, eval_dataset)

Supported Tasks and Leaderboards

The dataset is commonly used for evaluation of the image-super-resolution task.

Unofficial super-image leaderboard for:

• Scale 2
• Scale 3
• Scale 4
• Scale 8

Languages

Not applicable.

Dataset Structure

Data Instances

An example of validation for bicubic_x2 looks as follows.

{
    "hr": "/.cache/huggingface/datasets/downloads/extracted/Set5_HR/baby.png",
    "lr": "/.cache/huggingface/datasets/downloads/extracted/Set5_LR_x2/baby.png"
}

Data Fields

The data fields are the same among all splits.

• hr: a string to the path of the High Resolution (HR) .png image.
• lr: a string to the path of the Low Resolution (LR) .png image.

Data Splits

name	validation
bicubic_x2	5
bicubic_x3	5
bicubic_x4	5

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

No annotations.

Who are the annotators?

No annotators.

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

• Original Authors: Bevilacqua et al.

Licensing Information

Academic use only.

Citation Information

@article{bevilacqua2012low,
  title={Low-complexity single-image super-resolution based on nonnegative neighbor embedding},
  author={Bevilacqua, Marco and Roumy, Aline and Guillemot, Christine and Alberi-Morel, Marie Line},
  year={2012},
  publisher={BMVA press}
}

Contributions

Thanks to @eugenesiow for adding this dataset.